Transformer-based methods [1]}, [2]} have attracted more and more attention and achieved great performance in person ReID. For example, the pure transformer-based method TransReID [1]} achieves a significant performance boost over state-of-the-art CNN-based methods. However, there exists a large domain gap between ImageNet and person ReID datasets because 1) The image content of ImageNet and ReID datasets is very different [4]}; 2) supervised pre-training on ImageNet is focused on category-level supervision which reduces the rich visual information [5]} while person ReID prefers fine-grained identity information. As a result, Transformers need to be pre-trained on a larger-scale dataset ImageNet-21K [6]} to avoid over-fitting on the pre-training dataset. To bridge the gap between pre-training and fine-tuning datasets for better transformer-based ReID models, this paper tackles the problem from the perspectives of data and model structure, respectively.
